Machine learning applications span a very diverse landscape. Some areas include motor control, combinatorial search and optimization, clustering, prediction, anomaly detection, classification, regression, natural language processing, planning, and inference. A common thread is that a system learns the patterns and structure of the data in its environment, builds a model, and uses that model to make predictions of subsequent events and take action.
The models that emerge contain hundreds to trillions of continuously adaptive parameters. Human brains contain on the order of 1015 adaptive synapses. How the adaptive weights are exactly implemented in an algorithm varies and established methods include support vector machines, decision trees, artificial neural networks, and deep learning to name a few. Intuition tells us learning and modeling the environment is a valid approach in general, as the biological brain also appears to operate in this manner. The unfortunate limitation with our algorithmic approach, however, is that it runs on traditional digital hardware. In such a computer, calculations and memory updates must necessarily be performed in different physical locations, often separated by a significant distance.
The power required to adapt parameters grows impractically large as the number of parameters increases owing to the tremendous energy consumed shuttling digital bits back and forth. In a biological brain (and all of Nature), the processor and memory are the same physical substrate and computations and memory adaptations are performed in parallel. Recent progress has been made with multi-core processors and specialized parallel processing hardware like GP-GPUs, but for machine learning applications that intend to achieve the ultra-low power dissipation of biological nervous systems, it is a dead end approach.
The low-power solution to machine learning occurs when the memory-processor distance goes to zero, and this can only be achieved through intrinsically adaptive hardware.
Given the success of recent advancements in machine learning algorithms combined with the hardware power dilemma, an immense pressure exists for the development neuromorphic computer hardware. The Human Brain Project and the BRAIN Initiative with funding of over EUR 1.190 billion and USD 3 billion respectively partly aim to reverse engineer the brain in order to build brain-like hardware. DARPA's recent SyNAPSE program funded two large American tech companies—IBM Corporation and Hewlett Packard—as well as research giant HRL labs, and aimed to develop a new type of cognitive computer similar to the form and function of a mammalian brain. Cognimem is commercializing a k-nearest neighbor application specific integrated circuit (ASIC), a common machine learning task found in diverse applications. Stanford's Neurogrid, a computer board using mixed digital and analog computation to simulate a network, is yet another approach at neuromorphic hardware. Manchester University's SpiNNaker is another hardware platform utilizing parallel cores to simulate biologically realistic spiking neural networks. IBM Corporation's neurosynaptic core and TrueNorth cognitive computing system resulted from the SyNAPSE program. All these platforms have yet to prove utility along the path towards mass adoption and none have solved the foundational problem of memory-process separation.
More rigorous theoretical frameworks are also being developed for the neuromorphic computing field. For example, the idea of ‘universal memcomputing machines’ has been proposed as a general-purpose computing machine that has the same computational power as a non-deterministic Universal Turing Machine showing intrinsic parallelization and functional polymorphism. Such a system and other similar proposals employ a relatively new electronic component, the memristor, whose instantaneous state is a function of its past states. In other words, it has memory, and like a biological synapse, it can be used as a subcomponent for computation while at the same time storing a unit of data. A previous study has demonstrated that the memristor can better be used to implement neuromorphic hardware than traditional CMOS electronics.
Our attempt to develop neuromorphic hardware takes a unique approach inspired by life, and more generally, natural self-organization. We call the theoretical result of our efforts ‘AHaH Computing’. Rather than trying to reverse engineer the brain or transfer existing machine learning algorithms to new hardware and blindly hope to end up with an elegant power efficient chip, AHaH computing was designed from the beginning with a few key constraints: (1) must result in a hardware solution where memory and computation are combined, (2) must enable most or all machine learning applications, (3) must be simple enough to build chips with existing manufacturing technology and emulated with existing computational platforms, and (4) must be understandable and adoptable by application developers across all manufacturing sectors. This initial motivation led us to utilize physics and biology to create a technological framework for a neuromorphic processor satisfying the above constraints.
In trying to understand how Nature computes, we stumbled upon a fundamental structure found not only in the brain but also almost everywhere one looks—a self-organizing energy-dissipating fractal that we call ‘Knowm’. We find it in rivers, trees, lighting, and fungus, but we also find it deep within us. The air that we breathe is coupled to our blood through thousands of bifurcating flow channels that form our lungs. Our brain is coupled to our blood through thousands of bifurcating flow channels that form our arteries and veins. The neurons in our brains are built of thousands of bifurcating flow channels that form our axons and dendrites. At all scales of organization we see the same fractal built from the same simple building block: a simple structure formed of competing energy dissipation pathways. We call this building block Nature's Transistor′, as it appears to represent a foundational adaptive building block from which higher-order self-organized structures are built, much like the transistor is a building block for modern computing.
When multiple conduction pathways compete to dissipate energy through an adaptive container, the container will adapt in a particular way that leads to the maximization of energy dissipation. We call this mechanism the Anti-Hebbian and Hebbian (AHaH) plasticity rule. It is computationally universal, but perhaps more importantly and interestingly, it also leads to general-purpose solutions in machine learning.
Because the AHaH rule describes a physical process, we can create efficient and dense analog AHaH synaptic circuits with memristive components. One version of these mixed signal (digital and analog) circuits forms a generic adaptive computing resource we call Thermodynamic Random Access Memory or Thermodynamic-RAM. Thermodynamics is the branch of physics that describes the temporal evolution of matter as it flows from ordered to disordered states, and Nature's Transistor is an energy-dissipation flow structure, hence ‘thermodynamic’.
In neural systems, two things specify the algorithm: the network topology and the plasticity of the interconnections or synapses. Any general-purpose neural processor must contend with the problem that hard-wired neural topology will restrict the available neural algorithms that can be run on the processor. It is also crucial that the NPU interface merge easily with modern methods of computing. A ‘Random Access Synapse’ structure satisfies these constraints.